Introduction
With technology advancing relentlessly in the twenty-first century, more businesses are realising the potential of digital data. Consequently, business analytics teams are becoming as standard as finance, sales, and marketing departments which necessitates the need to hire data engineers.
However, although data availability is becoming increasingly crucial for accessing real-time insights, the analytical methods used could be newer. Hence, this explainer examines the differences between predictive and prescriptive analytics, two well-known data analytics categories out of the four main categories: diagnostic, prescriptive, predictive, and descriptive analysis.
What is Predictive Analytics?
Predictive analytics examines potential outcomes based on patterns, trends, and behaviors we've seen historically and currently. It analyses past and current data using data mining, statistical modeling, and artificial intelligence (AI).
The results include
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forecasts companies can use to reduce risk,
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anticipate customer weakening
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offer tailored product suggestions and more.
Based on their viewing history, Netflix's predictions about what a member is likely to watch next constitute one of the most well-known uses of predictive models. Predictive algorithms, however, are not just older than Netflix; credit scoring has been using them for decades.
Of course, predictive analytics has limitations. One such restriction is overfitting, where a predictive model becomes overly complex and starts to fit the noise in the data rather than the underlying patterns. This occurs when a predictive model only uses the data it was trained on.
Additionally, the model was trained using skewed or false data, which could produce discriminatory or erroneous forecasts.
What is Prescriptive Analytics?
Prescriptive analytics guides on ‘what should be done next.’ It takes information from various sources, applies artificial intelligence (AI) techniques (such as machine learning algorithms), and suggests the optimal course of action.
Prescriptive models allow businesses to assess the potential impact of a given decision on a business outcome and make necessary adjustments to improve chances of success. (However, it's crucial to remember that prescriptive analytics might not always be the ideal projection due to the inherent unpredictability of the future.)
Core Differences Between Predictive and Prescriptive Analytics
What are the primary distinctions between predictive and prescriptive analytics now that we know how they are used? Many resources attempt to distinguish clearly between prescriptive and predictive analytics.
Specifically, they are both components of a cohesive process rather than two entirely distinct processes, each stronger or ‘better’ than the other. Prescriptive and predictive analytics do, however, differ in a few ways. We'll review these in this section.
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Using historical data, predictive analytics makes predictions about possible future events.
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Prescriptive analytics generates precise, doable recommendations for these forecasts using various data.
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Structured historical data, including credit histories, transactional data, and customer information, is frequently used in predictive analytics. Hybrid data, which combines unstructured data (images, videos, and documents) with structured data (as previously mentioned), is frequently used in prescriptive analytics.
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From a statistical perspective, predictive analytics uses the outcomes of known independent variables to forecast the value of an unknown variable.
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Prescriptive analytics, on the other hand, focuses on finding the best value for a decision variable within a set of limitations to maximize one or more performance indicators.
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The same predictive analytics system will consistently produce the exact forecasts based on the same data.
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New data must be added regularly to ensure that the recommendations made by prescriptive analytics models remain current.
Prescriptive and predictive analytics share more characteristics with each other than set them apart, even with these distinctions. As such, trying to classify them too precisely can be challenging.
It's important to note that these two methods are typically used in conjunction with two more types of analytics. These are diagnostic analytics (figuring out why something happened) and descriptive analytics (figuring out what has happened).
When to Use Predictive Analytics
Practically any type of organization or industry, including
financial services,
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retail,
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services,
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public sector,
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medical care,
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and manufacturing,
can benefit from the optimistic potential of predictive analytics. It can increase revenue, improve operations, and reduce risk. Extensive data machine learning is sometimes utilized in augmented analytics.
When to Use Prescriptive Analytics
When you can make more informed judgments about what to do next with the empowering aid of prescriptive analytics, this can apply to any area of your company, including
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boosting sales,
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decreasing client weakening,
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stopping fraud,
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and boosting productivity.
Challenges and Considerations
Prescriptive analytics relies heavily on high-quality data since it provides the basis for precise, valuable recommendations. Bad data quality can produce wrong insights, undermining the models' efficacy.
When integrating disparate data sources, challenges such as
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conflicting formats,
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missing data,
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disparities in quality or granularity,
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and the need for data cleaning and normalization arises.
These problems must be fixed for the analysis to be coherent and trustworthy.
Implementing predictive analytics is a labor-intensive process that requires sophisticated tools, knowledgeable staff, and cutting-edge algorithms. This results in more significant expenses than predictive analytics, which is more concerned with forecasting than decision optimization.
Companies must believe in prescriptive analytics results because these suggestions influence choices. This belief is what we refer to as confidence in the model's predictions. Establishing this confidence requires a robust data governance system, consistent correctness, and model transparency. The potential benefits of prescriptive analytics may be limited if organizations lack this confidence and are reluctant to implement data-driven decisions.
Conclusion
Prescriptive analytics differs from predictive analytics in that the former offers immediate measurements to help comprehend what is happening within the organization. In contrast, the latter gives recommendations for action.
Predictive analytics measures metrics separately, not assessing their total significance. For instance, it can forecast and measure an organization's sales success but may not understand the effect of rising raw material prices on sales expenses and profitability.